Semiconductor devices such as logic and memory devices are typically fabricated by a sequence of processing steps applied to a specimen. The various features and multiple structural levels of the semiconductor devices are formed by these processing steps. For example, lithography among others is one semiconductor fabrication process that involves generating a pattern on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated on a single semiconductor wafer and then separated into individual semiconductor devices.
Optical metrology processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield. Optical metrology techniques offer the potential for high throughput without the risk of sample destruction. A number of optical metrology based techniques including scatterometry and reflectometry implementations and associated analysis algorithms are commonly used to characterize critical dimensions, film thicknesses, composition and other parameters of nanoscale structures.
As devices (e.g., logic and memory devices) move toward smaller nanometer-scale dimensions, characterization becomes more difficult. Devices incorporating complex three-dimensional geometry and materials with diverse physical properties contribute to characterization difficulty.
In response to these challenges, more complex optical tools have been developed. Multiple, different measurement technologies are available, and measurements are performed over a large ranges of several machine parameters (e.g., wavelength, azimuth and angle of incidence, etc.), and often simultaneously. As a result, the measurement time, computation time, and the overall time to generate reliable results, including measurement recipes, increases significantly.
Traditionally, the selection of measurement technique and the associated measurement recipe is performed on a trial-and-error basis. An experienced user manually selects various measurement techniques and recipes and performs an offline analysis to evaluate measurement efficacy. In some examples, measurement results are compared with reference measurement data, e.g., Tunneling Electron Microscope (TEM) data to determine measurement efficacy. In some other examples, a user performs model-based analysis to estimate measurement sensitivity to modelled parameters of interest, measurement precision of the modelled parameters of interest, and parameter correlation among different measurement subsystems. These results guide the user in the final determination of the measurement techniques and associated recipes to be used in a particular measurement application.
Traditional techniques for selecting the appropriate measurement techniques and associated recipes are limited. Model based sensitivity, precision and correlation analysis is limited by the number of points in parameter space that can be simulated in a reasonable period of time. These points may not accurately represent the actual measurement application. In another example, the sensitivity, precision and correlation estimates provide a complex picture of measurement efficacy that is difficult for users to interpret. As a result, it is difficult for a user to select the best measurement techniques and recipes from the simulation data currently provided. This allows the selection process to become reliant on user experience and bias, rather than rigorous analysis. Finally, traditional trial-and-error techniques are limited in the number of subsystems and recipe combinations that can be evaluated in a reasonable period of time. This limitation has become especially critical as the diversity of different measurement systems and recipe options have grown exponentially.
For example, the SpectraShape™ 10 K metrology platform available from KLA-Tencor Corporation, Milpitas, Calif., offers over twenty available measurement subsystems. The platform employs a Rotating Polarizer Spectroscopic Ellipsometry (RPSE) measurement technique with six subsystems having different azimuth angle (AA) and angle of Incidence (AOI) options. In addition, the platform employs a Rotating Polarizer Rotating Compensator (RPRC) Ellipsometry measurement technique with six subsystems having different AA and AOI options. In total, the platform offers more than twenty different subsystems with approximately one million available measurement subsystem and recipe combinations for the measurement of a critical dimension. Using available techniques, it is practically impossible for a user to identify and select the most effective combination of measurement subsystems and associated measurement recipes for a particular measurement application.
As the available range of optical metrology measurement subsystems and associated recipes has increased, so has the complexity of the measurement selection process. Improved methods and tools to streamline the identification and selection of the most effective combinations of measurement subsystems and associated measurement recipes for a particular measurement application are desired.